Behind every great conference is a team of dedicated reviewers. Congratulations to this year’s #CVPR2025 Outstanding Reviewers!
https://t.co/z8w4YJKTep
@yoshitomo_cs@CVPR Might be that the previous version did not get updated correctly, seems correct now after re-submission. Thanks a lot for the prompt response!
Home Robotics'te Beko ile işbirliğinin eşiğindeyiz. Lisans ve lisansüstü araştırmacılar arıyoruz. İlgilenenlerin bir an önce başvurmalarını (bazı ufak görevlerin yapılmasını bekleyeceğiz) öneriyorum.
Not: herkese ulaştırmak için takipçilerimin desteğini rica ediyorum.
If you're in Milan at #ECCV2024, check out our work #SceneTeller on high-quality 3D scene generation from natural language prompts!
🏡 Project page: https://t.co/Y1bjAk4YvX
📄 Paper: https://t.co/JZkP2ldZqB
👩💻 Code: https://t.co/PWj0EMy4QX
@eccvconf
Introducing The AI Scientist: The world’s first AI system for automating scientific research and open-ended discovery!
https://t.co/jC7g5GPVsE
From ideation, writing code, running experiments and summarizing results, to writing entire papers and conducting peer-review, The AI Scientist opens a new era of AI-driven scientific research and accelerated discovery.
Here are 4 example Machine Learning research papers generated by The AI Scientist.
We published our report, The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, and open-sourced our project!
Paper: https://t.co/lTQ8UenFHk
GitHub: https://t.co/Im53whVeAq
Our system leverages LLMs to propose and implement new research directions. Here, we first apply The AI Scientist to conduct Machine Learning research. Crucially, our system is capable of executing the entire ML research lifecycle: from inventing research ideas and experiments, writing code, to executing experiments on GPUs and gathering results. It can also write an entire scientific paper, explaining, visualizing and contextualizing the results.
Furthermore, while an LLM author writes entire research papers, another LLM reviewer critiques resulting manuscripts to provide feedback to improve the work, and also to select the most promising ideas to further develop in the next iteration cycle, leading to continual, open-ended discoveries, thus emulating the human scientific community. As a proof of concept, our system produced papers with novel contributions in ML research domains such language modeling, Diffusion and Grokking.
We (@_chris_lu_, @RobertTLange, @hardmaru) proudly collaborated with the @UniOfOxford (@j_foerst, @FLAIR_Ox) and @UBC (@cong_ml, @jeffclune) on this exciting project.
How to match the “wiggliness” of two shapes -- The blog post based on the project we worked with the fellows at MIT Summer Geometry Initiative is up!
https://t.co/4ZzWQ4qYAi